Overview

Dataset statistics

Number of variables36
Number of observations91199
Missing cells56
Missing cells (%)< 0.1%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory25.0 MiB
Average record size in memory288.0 B

Variable types

Unsupported2
Categorical11
Numeric23

Alerts

accident_year has constant value "2020" Constant
Dataset has 1 (< 0.1%) duplicate rowsDuplicates
date has a high cardinality: 366 distinct values High cardinality
time has a high cardinality: 1438 distinct values High cardinality
local_authority_ons_district has a high cardinality: 378 distinct values High cardinality
local_authority_highway has a high cardinality: 206 distinct values High cardinality
lsoa_of_accident_location has a high cardinality: 25931 distinct values High cardinality
location_easting_osgr is highly correlated with longitudeHigh correlation
location_northing_osgr is highly correlated with latitudeHigh correlation
longitude is highly correlated with location_easting_osgrHigh correlation
latitude is highly correlated with location_northing_osgrHigh correlation
police_force is highly correlated with local_authority_districtHigh correlation
local_authority_district is highly correlated with police_forceHigh correlation
first_road_class is highly correlated with first_road_numberHigh correlation
first_road_number is highly correlated with first_road_classHigh correlation
speed_limit is highly correlated with urban_or_rural_areaHigh correlation
junction_detail is highly correlated with junction_controlHigh correlation
junction_control is highly correlated with junction_detailHigh correlation
second_road_class is highly correlated with second_road_numberHigh correlation
second_road_number is highly correlated with second_road_classHigh correlation
weather_conditions is highly correlated with road_surface_conditionsHigh correlation
road_surface_conditions is highly correlated with weather_conditionsHigh correlation
urban_or_rural_area is highly correlated with speed_limitHigh correlation
location_easting_osgr is highly correlated with longitudeHigh correlation
location_northing_osgr is highly correlated with latitudeHigh correlation
longitude is highly correlated with location_easting_osgrHigh correlation
latitude is highly correlated with location_northing_osgrHigh correlation
police_force is highly correlated with local_authority_district and 1 other fieldsHigh correlation
local_authority_district is highly correlated with police_force and 1 other fieldsHigh correlation
speed_limit is highly correlated with urban_or_rural_areaHigh correlation
special_conditions_at_site is highly correlated with carriageway_hazardsHigh correlation
carriageway_hazards is highly correlated with special_conditions_at_siteHigh correlation
urban_or_rural_area is highly correlated with speed_limitHigh correlation
trunk_road_flag is highly correlated with police_force and 1 other fieldsHigh correlation
location_easting_osgr is highly correlated with longitudeHigh correlation
location_northing_osgr is highly correlated with latitudeHigh correlation
longitude is highly correlated with location_easting_osgrHigh correlation
latitude is highly correlated with location_northing_osgrHigh correlation
police_force is highly correlated with local_authority_districtHigh correlation
local_authority_district is highly correlated with police_forceHigh correlation
first_road_class is highly correlated with first_road_numberHigh correlation
first_road_number is highly correlated with first_road_classHigh correlation
speed_limit is highly correlated with urban_or_rural_areaHigh correlation
junction_detail is highly correlated with junction_controlHigh correlation
junction_control is highly correlated with junction_detailHigh correlation
second_road_class is highly correlated with second_road_numberHigh correlation
second_road_number is highly correlated with second_road_classHigh correlation
weather_conditions is highly correlated with road_surface_conditionsHigh correlation
road_surface_conditions is highly correlated with weather_conditionsHigh correlation
urban_or_rural_area is highly correlated with speed_limitHigh correlation
did_police_officer_attend_scene_of_accident is highly correlated with accident_yearHigh correlation
accident_severity is highly correlated with accident_yearHigh correlation
urban_or_rural_area is highly correlated with accident_yearHigh correlation
accident_year is highly correlated with did_police_officer_attend_scene_of_accident and 4 other fieldsHigh correlation
pedestrian_crossing_human_control is highly correlated with accident_yearHigh correlation
trunk_road_flag is highly correlated with accident_yearHigh correlation
location_easting_osgr is highly correlated with location_northing_osgr and 5 other fieldsHigh correlation
location_northing_osgr is highly correlated with location_easting_osgr and 6 other fieldsHigh correlation
longitude is highly correlated with location_easting_osgr and 5 other fieldsHigh correlation
latitude is highly correlated with location_easting_osgr and 6 other fieldsHigh correlation
police_force is highly correlated with location_easting_osgr and 6 other fieldsHigh correlation
local_authority_district is highly correlated with location_easting_osgr and 6 other fieldsHigh correlation
first_road_class is highly correlated with road_type and 1 other fieldsHigh correlation
first_road_number is highly correlated with location_northing_osgr and 2 other fieldsHigh correlation
road_type is highly correlated with first_road_class and 1 other fieldsHigh correlation
speed_limit is highly correlated with urban_or_rural_area and 1 other fieldsHigh correlation
junction_detail is highly correlated with junction_controlHigh correlation
junction_control is highly correlated with junction_detail and 1 other fieldsHigh correlation
second_road_class is highly correlated with junction_control and 1 other fieldsHigh correlation
second_road_number is highly correlated with second_road_classHigh correlation
pedestrian_crossing_human_control is highly correlated with pedestrian_crossing_physical_facilities and 2 other fieldsHigh correlation
pedestrian_crossing_physical_facilities is highly correlated with pedestrian_crossing_human_control and 1 other fieldsHigh correlation
light_conditions is highly correlated with weather_conditions and 1 other fieldsHigh correlation
weather_conditions is highly correlated with light_conditions and 2 other fieldsHigh correlation
road_surface_conditions is highly correlated with weather_conditionsHigh correlation
special_conditions_at_site is highly correlated with pedestrian_crossing_human_control and 2 other fieldsHigh correlation
carriageway_hazards is highly correlated with pedestrian_crossing_human_control and 1 other fieldsHigh correlation
urban_or_rural_area is highly correlated with speed_limit and 1 other fieldsHigh correlation
did_police_officer_attend_scene_of_accident is highly correlated with police_force and 2 other fieldsHigh correlation
trunk_road_flag is highly correlated with location_easting_osgr and 9 other fieldsHigh correlation
accident_index is an unsupported type, check if it needs cleaning or further analysis Unsupported
accident_reference is an unsupported type, check if it needs cleaning or further analysis Unsupported
first_road_number has 36524 (40.0%) zeros Zeros
junction_detail has 37978 (41.6%) zeros Zeros
second_road_number has 77627 (85.1%) zeros Zeros
pedestrian_crossing_physical_facilities has 69269 (76.0%) zeros Zeros
special_conditions_at_site has 87309 (95.7%) zeros Zeros
carriageway_hazards has 87881 (96.4%) zeros Zeros

Reproduction

Analysis started2022-02-22 14:05:49.185891
Analysis finished2022-02-22 14:07:42.680583
Duration1 minute and 53.49 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

accident_index
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size712.6 KiB

accident_year
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size712.6 KiB
2020
91199 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
202091199
100.0%

Length

2022-02-22T15:07:42.777937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-22T15:07:42.856906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
202091199
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

accident_reference
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size712.6 KiB

location_easting_osgr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct75403
Distinct (%)82.7%
Missing14
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean456487.8764
Minimum65947
Maximum655138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size712.6 KiB
2022-02-22T15:07:42.948413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum65947
5-th percentile279683.2
Q1392890
median465545
Q3530168
95-th percentile581225.8
Maximum655138
Range589191
Interquartile range (IQR)137278

Descriptive statistics

Standard deviation93512.71181
Coefficient of variation (CV)0.2048525638
Kurtosis-0.1485284961
Mean456487.8764
Median Absolute Deviation (MAD)65781
Skewness-0.5775405017
Sum4.162484701 × 1010
Variance8744627269
MonotonicityNot monotonic
2022-02-22T15:07:43.092775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5319498
 
< 0.1%
5342468
 
< 0.1%
5338087
 
< 0.1%
5317967
 
< 0.1%
5270867
 
< 0.1%
5340607
 
< 0.1%
5344237
 
< 0.1%
5324116
 
< 0.1%
4581656
 
< 0.1%
5311816
 
< 0.1%
Other values (75393)91116
99.9%
(Missing)14
 
< 0.1%
ValueCountFrequency (%)
659471
< 0.1%
715001
< 0.1%
757201
< 0.1%
784011
< 0.1%
821221
< 0.1%
1134101
< 0.1%
1293041
< 0.1%
1299951
< 0.1%
1303741
< 0.1%
1313281
< 0.1%
ValueCountFrequency (%)
6551381
< 0.1%
6551311
< 0.1%
6548201
< 0.1%
6547711
< 0.1%
6546951
< 0.1%
6546932
< 0.1%
6546601
< 0.1%
6546131
< 0.1%
6545581
< 0.1%
6545361
< 0.1%

location_northing_osgr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct75498
Distinct (%)82.8%
Missing14
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean273764.4962
Minimum12715
Maximum1184351
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size712.6 KiB
2022-02-22T15:07:43.232101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12715
5-th percentile103654.2
Q1174569
median208599
Q3378366
95-th percentile565786.6
Maximum1184351
Range1171636
Interquartile range (IQR)203797

Descriptive statistics

Standard deviation147351.5561
Coefficient of variation (CV)0.5382420224
Kurtosis1.33924042
Mean273764.4962
Median Absolute Deviation (MAD)74450
Skewness1.187215511
Sum2.496321559 × 1010
Variance2.171248109 × 1010
MonotonicityNot monotonic
2022-02-22T15:07:43.381353image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1828439
 
< 0.1%
1835628
 
< 0.1%
1865157
 
< 0.1%
1816027
 
< 0.1%
1785797
 
< 0.1%
1851937
 
< 0.1%
1831197
 
< 0.1%
1777086
 
< 0.1%
1848006
 
< 0.1%
1862696
 
< 0.1%
Other values (75488)91115
99.9%
(Missing)14
 
< 0.1%
ValueCountFrequency (%)
127151
< 0.1%
150461
< 0.1%
153641
< 0.1%
158371
< 0.1%
172301
< 0.1%
179681
< 0.1%
192381
< 0.1%
198131
< 0.1%
198951
< 0.1%
199541
< 0.1%
ValueCountFrequency (%)
11843511
< 0.1%
11527791
< 0.1%
11522561
< 0.1%
11454161
< 0.1%
11423821
< 0.1%
11401561
< 0.1%
11397621
< 0.1%
11382251
< 0.1%
11362761
< 0.1%
11358171
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct89589
Distinct (%)98.2%
Missing14
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-1.189257945
Minimum-7.497375
Maximum1.756257
Zeros0
Zeros (%)0.0%
Negative77289
Negative (%)84.7%
Memory size712.6 KiB
2022-02-22T15:07:43.527923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-7.497375
5-th percentile-3.8163898
Q1-2.107789
median-1.046912
Q3-0.125238
95-th percentile0.608147
Maximum1.756257
Range9.253632
Interquartile range (IQR)1.982551

Descriptive statistics

Standard deviation1.36778622
Coefficient of variation (CV)-1.15011737
Kurtosis-0.1042803005
Mean-1.189257945
Median Absolute Deviation (MAD)0.942809
Skewness-0.6049187827
Sum-108442.4857
Variance1.870839143
MonotonicityNot monotonic
2022-02-22T15:07:43.665989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.549114
 
< 0.1%
-1.8938434
 
< 0.1%
-0.071894
 
< 0.1%
-0.0749863
 
< 0.1%
-2.5930993
 
< 0.1%
-0.049893
 
< 0.1%
0.0112453
 
< 0.1%
-2.0281893
 
< 0.1%
0.0838973
 
< 0.1%
-0.1676723
 
< 0.1%
Other values (89579)91152
99.9%
(Missing)14
 
< 0.1%
ValueCountFrequency (%)
-7.4973751
< 0.1%
-7.4128121
< 0.1%
-7.3789251
< 0.1%
-7.3213851
< 0.1%
-7.3061621
< 0.1%
-6.8426551
< 0.1%
-6.506071
< 0.1%
-6.505581
< 0.1%
-6.5042721
< 0.1%
-6.4988041
< 0.1%
ValueCountFrequency (%)
1.7562571
< 0.1%
1.7557491
< 0.1%
1.7513411
< 0.1%
1.7507591
< 0.1%
1.7503911
< 0.1%
1.7494861
< 0.1%
1.7491681
< 0.1%
1.7491641
< 0.1%
1.7491361
< 0.1%
1.7474181
< 0.1%

latitude
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct88748
Distinct (%)97.3%
Missing14
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean52.3510732
Minimum49.970479
Maximum60.541144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size712.6 KiB
2022-02-22T15:07:43.827071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum49.970479
5-th percentile50.8207238
Q151.457237
median51.763385
Q353.297386
95-th percentile54.9857186
Maximum60.541144
Range10.570665
Interquartile range (IQR)1.840149

Descriptive statistics

Standard deviation1.327572787
Coefficient of variation (CV)0.02535903671
Kurtosis1.301830994
Mean52.3510732
Median Absolute Deviation (MAD)0.676633
Skewness1.178717506
Sum4773632.61
Variance1.762449505
MonotonicityNot monotonic
2022-02-22T15:07:44.001061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.5417794
 
< 0.1%
51.5508434
 
< 0.1%
51.5220784
 
< 0.1%
53.7997084
 
< 0.1%
51.5246743
 
< 0.1%
51.4497793
 
< 0.1%
52.6005383
 
< 0.1%
51.4026743
 
< 0.1%
51.5171513
 
< 0.1%
50.8499763
 
< 0.1%
Other values (88738)91151
99.9%
(Missing)14
 
< 0.1%
ValueCountFrequency (%)
49.9704791
< 0.1%
49.9911421
< 0.1%
49.9939781
< 0.1%
49.9981691
< 0.1%
50.011581
< 0.1%
50.0194971
< 0.1%
50.0279841
< 0.1%
50.0362091
< 0.1%
50.036621
< 0.1%
50.0370381
< 0.1%
ValueCountFrequency (%)
60.5411441
< 0.1%
60.2571
< 0.1%
60.2527111
< 0.1%
60.1909641
< 0.1%
60.1632811
< 0.1%
60.1433811
< 0.1%
60.1402941
< 0.1%
60.126621
< 0.1%
60.1092111
< 0.1%
60.1052961
< 0.1%

police_force
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct44
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.48804263
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size712.6 KiB
2022-02-22T15:07:44.166907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median22
Q345
95-th percentile63
Maximum99
Range98
Interquartile range (IQR)41

Descriptive statistics

Standard deviation24.54896379
Coefficient of variation (CV)0.8930779144
Kurtosis0.6762674699
Mean27.48804263
Median Absolute Deviation (MAD)21
Skewness0.8963433425
Sum2506882
Variance602.651623
MonotonicityIncreasing
2022-02-22T15:07:44.318361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
120906
22.9%
203933
 
4.3%
993836
 
4.2%
463405
 
3.7%
473107
 
3.4%
443016
 
3.3%
432767
 
3.0%
132764
 
3.0%
502616
 
2.9%
422536
 
2.8%
Other values (34)42313
46.4%
ValueCountFrequency (%)
120906
22.9%
3755
 
0.8%
42354
 
2.6%
51717
 
1.9%
62399
 
2.6%
71363
 
1.5%
101690
 
1.9%
11601
 
0.7%
121180
 
1.3%
132764
 
3.0%
ValueCountFrequency (%)
993836
4.2%
63771
 
0.8%
62958
 
1.1%
61487
 
0.5%
60658
 
0.7%
551158
 
1.3%
54961
 
1.1%
53695
 
0.8%
522044
2.2%
502616
2.9%

accident_severity
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size712.6 KiB
3
71453 
2
18355 
1
 
1391

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
371453
78.3%
218355
 
20.1%
11391
 
1.5%

Length

2022-02-22T15:07:44.578590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-22T15:07:44.648318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
371453
78.3%
218355
 
20.1%
11391
 
1.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

number_of_vehicles
Real number (ℝ≥0)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.835272317
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size712.6 KiB
2022-02-22T15:07:44.717499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum13
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6772721133
Coefficient of variation (CV)0.3690308556
Kurtosis9.143359462
Mean1.835272317
Median Absolute Deviation (MAD)0
Skewness1.517952791
Sum167375
Variance0.4586975154
MonotonicityNot monotonic
2022-02-22T15:07:44.830840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
257392
62.9%
125730
28.2%
36241
 
6.8%
41334
 
1.5%
5331
 
0.4%
699
 
0.1%
747
 
0.1%
815
 
< 0.1%
93
 
< 0.1%
103
 
< 0.1%
Other values (3)4
 
< 0.1%
ValueCountFrequency (%)
125730
28.2%
257392
62.9%
36241
 
6.8%
41334
 
1.5%
5331
 
0.4%
699
 
0.1%
747
 
0.1%
815
 
< 0.1%
93
 
< 0.1%
103
 
< 0.1%
ValueCountFrequency (%)
131
 
< 0.1%
121
 
< 0.1%
112
 
< 0.1%
103
 
< 0.1%
93
 
< 0.1%
815
 
< 0.1%
747
 
0.1%
699
 
0.1%
5331
 
0.4%
41334
1.5%

number_of_casualties
Real number (ℝ≥0)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.267382318
Minimum1
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size712.6 KiB
2022-02-22T15:07:44.952141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum41
Range40
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6814732733
Coefficient of variation (CV)0.5377014211
Kurtosis152.3683194
Mean1.267382318
Median Absolute Deviation (MAD)0
Skewness5.837364345
Sum115584
Variance0.4644058223
MonotonicityNot monotonic
2022-02-22T15:07:45.064046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
174161
81.3%
212221
 
13.4%
33214
 
3.5%
41063
 
1.2%
5349
 
0.4%
6115
 
0.1%
743
 
< 0.1%
814
 
< 0.1%
97
 
< 0.1%
114
 
< 0.1%
Other values (6)8
 
< 0.1%
ValueCountFrequency (%)
174161
81.3%
212221
 
13.4%
33214
 
3.5%
41063
 
1.2%
5349
 
0.4%
6115
 
0.1%
743
 
< 0.1%
814
 
< 0.1%
97
 
< 0.1%
103
 
< 0.1%
ValueCountFrequency (%)
411
 
< 0.1%
191
 
< 0.1%
171
 
< 0.1%
131
 
< 0.1%
121
 
< 0.1%
114
 
< 0.1%
103
 
< 0.1%
97
 
< 0.1%
814
 
< 0.1%
743
< 0.1%

date
Categorical

HIGH CARDINALITY

Distinct366
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size712.6 KiB
06/02/2020
 
426
04/11/2020
 
414
06/03/2020
 
411
21/01/2020
 
399
10/01/2020
 
397
Other values (361)
89152 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row04/02/2020
2nd row27/04/2020
3rd row01/01/2020
4th row01/01/2020
5th row01/01/2020

Common Values

ValueCountFrequency (%)
06/02/2020426
 
0.5%
04/11/2020414
 
0.5%
06/03/2020411
 
0.5%
21/01/2020399
 
0.4%
10/01/2020397
 
0.4%
18/09/2020397
 
0.4%
17/01/2020396
 
0.4%
14/01/2020395
 
0.4%
07/02/2020391
 
0.4%
20/01/2020386
 
0.4%
Other values (356)87187
95.6%

Length

2022-02-22T15:07:45.185997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
06/02/2020426
 
0.5%
04/11/2020414
 
0.5%
06/03/2020411
 
0.5%
21/01/2020399
 
0.4%
10/01/2020397
 
0.4%
18/09/2020397
 
0.4%
17/01/2020396
 
0.4%
14/01/2020395
 
0.4%
07/02/2020391
 
0.4%
20/01/2020386
 
0.4%
Other values (356)87187
95.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

day_of_week
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.12155835
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size712.6 KiB
2022-02-22T15:07:45.284014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.932200224
Coefficient of variation (CV)0.4688033167
Kurtosis-1.190798175
Mean4.12155835
Median Absolute Deviation (MAD)2
Skewness-0.0790988166
Sum375882
Variance3.733397707
MonotonicityNot monotonic
2022-02-22T15:07:45.384615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
614889
16.3%
514056
15.4%
413564
14.9%
313267
14.5%
212772
14.0%
712336
13.5%
110315
11.3%
ValueCountFrequency (%)
110315
11.3%
212772
14.0%
313267
14.5%
413564
14.9%
514056
15.4%
614889
16.3%
712336
13.5%
ValueCountFrequency (%)
712336
13.5%
614889
16.3%
514056
15.4%
413564
14.9%
313267
14.5%
212772
14.0%
110315
11.3%

time
Categorical

HIGH CARDINALITY

Distinct1438
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size712.6 KiB
17:00
 
862
16:00
 
785
15:00
 
774
17:30
 
746
18:00
 
739
Other values (1433)
87293 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row09:00
2nd row13:55
3rd row01:25
4th row01:50
5th row02:25

Common Values

ValueCountFrequency (%)
17:00862
 
0.9%
16:00785
 
0.9%
15:00774
 
0.8%
17:30746
 
0.8%
18:00739
 
0.8%
14:00699
 
0.8%
16:30697
 
0.8%
15:30697
 
0.8%
18:30629
 
0.7%
13:00605
 
0.7%
Other values (1428)83966
92.1%

Length

2022-02-22T15:07:45.501945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17:00862
 
0.9%
16:00785
 
0.9%
15:00774
 
0.8%
17:30746
 
0.8%
18:00739
 
0.8%
14:00699
 
0.8%
16:30697
 
0.8%
15:30697
 
0.8%
18:30629
 
0.7%
13:00605
 
0.7%
Other values (1428)83966
92.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

local_authority_district
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct377
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean311.4828123
Minimum-1
Maximum941
Zeros0
Zeros (%)0.0%
Negative991
Negative (%)1.1%
Memory size712.6 KiB
2022-02-22T15:07:45.630565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile5
Q163
median300
Q3502
95-th percentile750
Maximum941
Range942
Interquartile range (IQR)439

Descriptive statistics

Standard deviation253.4563293
Coefficient of variation (CV)0.8137088765
Kurtosis-0.5518546196
Mean311.4828123
Median Absolute Deviation (MAD)218
Skewness0.5069198558
Sum28406921
Variance64240.11084
MonotonicityNot monotonic
2022-02-22T15:07:45.784538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3001801
 
2.0%
91063
 
1.2%
11029
 
1.1%
204997
 
1.1%
-1991
 
1.1%
10932
 
1.0%
5897
 
1.0%
20892
 
1.0%
8888
 
1.0%
32863
 
0.9%
Other values (367)80846
88.6%
ValueCountFrequency (%)
-1991
1.1%
11029
1.1%
2613
0.7%
3565
0.6%
4790
0.9%
5897
1.0%
6609
0.7%
7758
0.8%
8888
1.0%
91063
1.2%
ValueCountFrequency (%)
94113
 
< 0.1%
940137
0.2%
93979
 
0.1%
938229
0.3%
93776
 
0.1%
93611
 
< 0.1%
935120
0.1%
934128
0.1%
9339
 
< 0.1%
932187
0.2%

local_authority_ons_district
Categorical

HIGH CARDINALITY

Distinct378
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size712.6 KiB
E08000025
 
1802
E09000022
 
1054
E09000033
 
1037
E08000035
 
1009
E09000032
 
950
Other values (373)
85347 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE09000032
2nd rowE09000022
3rd rowE09000033
4th rowE09000025
5th rowE09000033

Common Values

ValueCountFrequency (%)
E080000251802
 
2.0%
E090000221054
 
1.2%
E090000331037
 
1.1%
E080000351009
 
1.1%
E09000032950
 
1.0%
E09000030896
 
1.0%
E09000028893
 
1.0%
E09000008892
 
1.0%
E06000052862
 
0.9%
E09000010862
 
0.9%
Other values (368)80942
88.8%

Length

2022-02-22T15:07:45.922416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e080000251802
 
2.0%
e090000221054
 
1.2%
e090000331037
 
1.1%
e080000351009
 
1.1%
e09000032950
 
1.0%
e09000030896
 
1.0%
e09000028893
 
1.0%
e09000008892
 
1.0%
e06000052862
 
0.9%
e09000010862
 
0.9%
Other values (368)80942
88.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

local_authority_highway
Categorical

HIGH CARDINALITY

Distinct206
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size712.6 KiB
E10000016
 
2964
E10000030
 
2334
E10000012
 
2064
E10000014
 
1993
E10000017
 
1840
Other values (201)
80004 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE09000032
2nd rowE09000022
3rd rowE09000033
4th rowE09000025
5th rowE09000033

Common Values

ValueCountFrequency (%)
E100000162964
 
3.3%
E100000302334
 
2.6%
E100000122064
 
2.3%
E100000141993
 
2.2%
E100000171840
 
2.0%
E080000251802
 
2.0%
E100000321566
 
1.7%
E100000151398
 
1.5%
E100000191396
 
1.5%
E100000201324
 
1.5%
Other values (196)72518
79.5%

Length

2022-02-22T15:07:46.023082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e100000162964
 
3.3%
e100000302334
 
2.6%
e100000122064
 
2.3%
e100000141993
 
2.2%
e100000171840
 
2.0%
e080000251802
 
2.0%
e100000321566
 
1.7%
e100000151398
 
1.5%
e100000191396
 
1.5%
e100000201324
 
1.5%
Other values (196)72518
79.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

first_road_class
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.220320398
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size712.6 KiB
2022-02-22T15:07:46.117686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13
median4
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.44347545
Coefficient of variation (CV)0.3420298257
Kurtosis-1.307058069
Mean4.220320398
Median Absolute Deviation (MAD)1
Skewness0.09317430526
Sum384889
Variance2.083621376
MonotonicityNot monotonic
2022-02-22T15:07:46.221653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
340604
44.5%
631709
34.8%
411490
 
12.6%
54815
 
5.3%
12374
 
2.6%
2207
 
0.2%
ValueCountFrequency (%)
12374
 
2.6%
2207
 
0.2%
340604
44.5%
411490
 
12.6%
54815
 
5.3%
631709
34.8%
ValueCountFrequency (%)
631709
34.8%
54815
 
5.3%
411490
 
12.6%
340604
44.5%
2207
 
0.2%
12374
 
2.6%

first_road_number
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct3068
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean790.6660709
Minimum0
Maximum9174
Zeros36524
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size712.6 KiB
2022-02-22T15:07:46.350938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median34
Q3538
95-th percentile5058
Maximum9174
Range9174
Interquartile range (IQR)538

Descriptive statistics

Standard deviation1580.817743
Coefficient of variation (CV)1.999349411
Kurtosis4.261239008
Mean790.6660709
Median Absolute Deviation (MAD)34
Skewness2.290483161
Sum72107955
Variance2498984.737
MonotonicityNot monotonic
2022-02-22T15:07:46.496878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
036524
40.0%
1926
 
1.0%
6660
 
0.7%
4578
 
0.6%
3550
 
0.6%
23546
 
0.6%
5535
 
0.6%
2486
 
0.5%
38477
 
0.5%
40474
 
0.5%
Other values (3058)49443
54.2%
ValueCountFrequency (%)
036524
40.0%
1926
 
1.0%
2486
 
0.5%
3550
 
0.6%
4578
 
0.6%
5535
 
0.6%
6660
 
0.7%
732
 
< 0.1%
8136
 
0.1%
967
 
0.1%
ValueCountFrequency (%)
91742
< 0.1%
91704
< 0.1%
91652
< 0.1%
91631
 
< 0.1%
91573
< 0.1%
91551
 
< 0.1%
91524
< 0.1%
91421
 
< 0.1%
91402
< 0.1%
91323
< 0.1%

road_type
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.256000614
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size712.6 KiB
2022-02-22T15:07:46.624660image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median6
Q36
95-th percentile6
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.684878019
Coefficient of variation (CV)0.3205627516
Kurtosis0.8405394475
Mean5.256000614
Median Absolute Deviation (MAD)0
Skewness-1.166454629
Sum479342
Variance2.83881394
MonotonicityNot monotonic
2022-02-22T15:07:46.721372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
666929
73.4%
313206
 
14.5%
15580
 
6.1%
92006
 
2.2%
21966
 
2.2%
71512
 
1.7%
ValueCountFrequency (%)
15580
 
6.1%
21966
 
2.2%
313206
 
14.5%
666929
73.4%
71512
 
1.7%
92006
 
2.2%
ValueCountFrequency (%)
92006
 
2.2%
71512
 
1.7%
666929
73.4%
313206
 
14.5%
21966
 
2.2%
15580
 
6.1%

speed_limit
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.27011261
Minimum-1
Maximum70
Zeros0
Zeros (%)0.0%
Negative12
Negative (%)< 0.1%
Memory size712.6 KiB
2022-02-22T15:07:46.827254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile20
Q130
median30
Q340
95-th percentile70
Maximum70
Range71
Interquartile range (IQR)10

Descriptive statistics

Standard deviation13.89603165
Coefficient of variation (CV)0.383126234
Kurtosis0.1488941296
Mean36.27011261
Median Absolute Deviation (MAD)0
Skewness1.14761133
Sum3307798
Variance193.0996957
MonotonicityNot monotonic
2022-02-22T15:07:47.036111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3052260
57.3%
6011408
 
12.5%
2011183
 
12.3%
407867
 
8.6%
704687
 
5.1%
503782
 
4.1%
-112
 
< 0.1%
ValueCountFrequency (%)
-112
 
< 0.1%
2011183
 
12.3%
3052260
57.3%
407867
 
8.6%
503782
 
4.1%
6011408
 
12.5%
704687
 
5.1%
ValueCountFrequency (%)
704687
 
5.1%
6011408
 
12.5%
503782
 
4.1%
407867
 
8.6%
3052260
57.3%
2011183
 
12.3%
-112
 
< 0.1%

junction_detail
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.934878672
Minimum-1
Maximum99
Zeros37978
Zeros (%)41.6%
Negative2
Negative (%)< 0.1%
Memory size712.6 KiB
2022-02-22T15:07:47.142720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median2
Q33
95-th percentile9
Maximum99
Range100
Interquartile range (IQR)3

Descriptive statistics

Standard deviation12.61277739
Coefficient of variation (CV)3.205378983
Kurtosis50.37446522
Mean3.934878672
Median Absolute Deviation (MAD)2
Skewness7.071211171
Sum358857
Variance159.0821536
MonotonicityNot monotonic
2022-02-22T15:07:47.247454image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
037978
41.6%
325626
28.1%
68429
 
9.2%
17326
 
8.0%
94705
 
5.2%
81887
 
2.1%
991508
 
1.7%
21300
 
1.4%
71279
 
1.4%
51159
 
1.3%
ValueCountFrequency (%)
-12
 
< 0.1%
037978
41.6%
17326
 
8.0%
21300
 
1.4%
325626
28.1%
51159
 
1.3%
68429
 
9.2%
71279
 
1.4%
81887
 
2.1%
94705
 
5.2%
ValueCountFrequency (%)
991508
 
1.7%
94705
 
5.2%
81887
 
2.1%
71279
 
1.4%
68429
 
9.2%
51159
 
1.3%
325626
28.1%
21300
 
1.4%
17326
 
8.0%
037978
41.6%

junction_control
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.737497122
Minimum-1
Maximum9
Zeros0
Zeros (%)0.0%
Negative38298
Negative (%)42.0%
Memory size712.6 KiB
2022-02-22T15:07:47.350575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median2
Q34
95-th percentile4
Maximum9
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.510124854
Coefficient of variation (CV)1.444678568
Kurtosis-0.9357325121
Mean1.737497122
Median Absolute Deviation (MAD)2
Skewness0.18522575
Sum158458
Variance6.300726782
MonotonicityNot monotonic
2022-02-22T15:07:47.445247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
439998
43.9%
-138298
42.0%
210407
 
11.4%
91536
 
1.7%
3583
 
0.6%
1377
 
0.4%
ValueCountFrequency (%)
-138298
42.0%
1377
 
0.4%
210407
 
11.4%
3583
 
0.6%
439998
43.9%
91536
 
1.7%
ValueCountFrequency (%)
91536
 
1.7%
439998
43.9%
3583
 
0.6%
210407
 
11.4%
1377
 
0.4%
-138298
42.0%

second_road_class
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.551771401
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size712.6 KiB
2022-02-22T15:07:47.533868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q16
median6
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.015112626
Coefficient of variation (CV)0.182844817
Kurtosis2.892798422
Mean5.551771401
Median Absolute Deviation (MAD)0
Skewness-2.072029219
Sum506316
Variance1.030453643
MonotonicityNot monotonic
2022-02-22T15:07:47.631835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
674473
81.7%
39566
 
10.5%
43656
 
4.0%
53154
 
3.5%
1314
 
0.3%
236
 
< 0.1%
ValueCountFrequency (%)
1314
 
0.3%
236
 
< 0.1%
39566
 
10.5%
43656
 
4.0%
53154
 
3.5%
674473
81.7%
ValueCountFrequency (%)
674473
81.7%
53154
 
3.5%
43656
 
4.0%
39566
 
10.5%
236
 
< 0.1%
1314
 
0.3%

second_road_number
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2274
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean220.2319872
Minimum-1
Maximum9174
Zeros77627
Zeros (%)85.1%
Negative7
Negative (%)< 0.1%
Memory size712.6 KiB
2022-02-22T15:07:47.756030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q30
95-th percentile1124
Maximum9174
Range9175
Interquartile range (IQR)0

Descriptive statistics

Standard deviation913.6928324
Coefficient of variation (CV)4.148774408
Kurtosis25.65555285
Mean220.2319872
Median Absolute Deviation (MAD)0
Skewness4.997485408
Sum20084937
Variance834834.5919
MonotonicityNot monotonic
2022-02-22T15:07:47.900433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
077627
85.1%
1158
 
0.2%
406150
 
0.2%
4148
 
0.2%
41120
 
0.1%
3119
 
0.1%
40118
 
0.1%
2114
 
0.1%
6103
 
0.1%
23103
 
0.1%
Other values (2264)12439
 
13.6%
ValueCountFrequency (%)
-17
 
< 0.1%
077627
85.1%
1158
 
0.2%
2114
 
0.1%
3119
 
0.1%
4148
 
0.2%
577
 
0.1%
6103
 
0.1%
710
 
< 0.1%
833
 
< 0.1%
ValueCountFrequency (%)
91743
< 0.1%
91701
 
< 0.1%
91571
 
< 0.1%
91491
 
< 0.1%
91271
 
< 0.1%
91201
 
< 0.1%
91191
 
< 0.1%
90801
 
< 0.1%
90311
 
< 0.1%
90231
 
< 0.1%

pedestrian_crossing_human_control
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size712.6 KiB
0
86358 
9
 
3332
2
 
980
1
 
386
-1
 
143

Length

Max length2
Median length1
Mean length1.001568
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
086358
94.7%
93332
 
3.7%
2980
 
1.1%
1386
 
0.4%
-1143
 
0.2%

Length

2022-02-22T15:07:48.030357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-22T15:07:48.099005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
086358
94.7%
93332
 
3.7%
2980
 
1.1%
1529
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

pedestrian_crossing_physical_facilities
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.185309049
Minimum-1
Maximum9
Zeros69269
Zeros (%)76.0%
Negative135
Negative (%)0.1%
Memory size712.6 KiB
2022-02-22T15:07:48.175767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q30
95-th percentile8
Maximum9
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.44592447
Coefficient of variation (CV)2.063533111
Kurtosis2.666856343
Mean1.185309049
Median Absolute Deviation (MAD)0
Skewness1.966227369
Sum108099
Variance5.982546515
MonotonicityNot monotonic
2022-02-22T15:07:48.271713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
069269
76.0%
57465
 
8.2%
44903
 
5.4%
13836
 
4.2%
92932
 
3.2%
82460
 
2.7%
7199
 
0.2%
-1135
 
0.1%
ValueCountFrequency (%)
-1135
 
0.1%
069269
76.0%
13836
 
4.2%
44903
 
5.4%
57465
 
8.2%
7199
 
0.2%
82460
 
2.7%
92932
 
3.2%
ValueCountFrequency (%)
92932
 
3.2%
82460
 
2.7%
7199
 
0.2%
57465
 
8.2%
44903
 
5.4%
13836
 
4.2%
069269
76.0%
-1135
 
0.1%

light_conditions
Real number (ℝ)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.065307734
Minimum-1
Maximum7
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size712.6 KiB
2022-02-22T15:07:48.366855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q11
median1
Q34
95-th percentile6
Maximum7
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.747690004
Coefficient of variation (CV)0.8462128794
Kurtosis0.3619659419
Mean2.065307734
Median Absolute Deviation (MAD)0
Skewness1.305012031
Sum188354
Variance3.054420351
MonotonicityNot monotonic
2022-02-22T15:07:48.463064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
164458
70.7%
419026
 
20.9%
64835
 
5.3%
72194
 
2.4%
5685
 
0.8%
-11
 
< 0.1%
ValueCountFrequency (%)
-11
 
< 0.1%
164458
70.7%
419026
 
20.9%
5685
 
0.8%
64835
 
5.3%
72194
 
2.4%
ValueCountFrequency (%)
72194
 
2.4%
64835
 
5.3%
5685
 
0.8%
419026
 
20.9%
164458
70.7%
-11
 
< 0.1%

weather_conditions
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.702047172
Minimum-1
Maximum9
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size712.6 KiB
2022-02-22T15:07:48.563736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q11
median1
Q31
95-th percentile8
Maximum9
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.845785686
Coefficient of variation (CV)1.084450371
Kurtosis8.043908159
Mean1.702047172
Median Absolute Deviation (MAD)0
Skewness3.022075815
Sum155225
Variance3.406924799
MonotonicityNot monotonic
2022-02-22T15:07:48.657480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
170729
77.6%
211583
 
12.7%
82629
 
2.9%
92423
 
2.7%
51665
 
1.8%
41401
 
1.5%
7510
 
0.6%
3185
 
0.2%
673
 
0.1%
-11
 
< 0.1%
ValueCountFrequency (%)
-11
 
< 0.1%
170729
77.6%
211583
 
12.7%
3185
 
0.2%
41401
 
1.5%
51665
 
1.8%
673
 
0.1%
7510
 
0.6%
82629
 
2.9%
92423
 
2.7%
ValueCountFrequency (%)
92423
 
2.7%
82629
 
2.9%
7510
 
0.6%
673
 
0.1%
51665
 
1.8%
41401
 
1.5%
3185
 
0.2%
211583
 
12.7%
170729
77.6%
-11
 
< 0.1%

road_surface_conditions
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.391583241
Minimum-1
Maximum9
Zeros0
Zeros (%)0.0%
Negative316
Negative (%)0.3%
Memory size712.6 KiB
2022-02-22T15:07:48.749974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q11
median1
Q32
95-th percentile2
Maximum9
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9256901947
Coefficient of variation (CV)0.6652064838
Kurtosis40.62687353
Mean1.391583241
Median Absolute Deviation (MAD)0
Skewness5.442426324
Sum126911
Variance0.8569023366
MonotonicityNot monotonic
2022-02-22T15:07:48.842894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
162698
68.7%
226240
28.8%
9847
 
0.9%
4764
 
0.8%
-1316
 
0.3%
5184
 
0.2%
3150
 
0.2%
ValueCountFrequency (%)
-1316
 
0.3%
162698
68.7%
226240
28.8%
3150
 
0.2%
4764
 
0.8%
5184
 
0.2%
9847
 
0.9%
ValueCountFrequency (%)
9847
 
0.9%
5184
 
0.2%
4764
 
0.8%
3150
 
0.2%
226240
28.8%
162698
68.7%
-1316
 
0.3%

special_conditions_at_site
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2447395257
Minimum-1
Maximum9
Zeros87309
Zeros (%)95.7%
Negative218
Negative (%)0.2%
Memory size712.6 KiB
2022-02-22T15:07:48.942035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.318553879
Coefficient of variation (CV)5.387580429
Kurtosis32.20701676
Mean0.2447395257
Median Absolute Deviation (MAD)0
Skewness5.673150744
Sum22320
Variance1.738584331
MonotonicityNot monotonic
2022-02-22T15:07:49.042008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
087309
95.7%
91452
 
1.6%
41106
 
1.2%
7317
 
0.3%
5228
 
0.3%
-1218
 
0.2%
1213
 
0.2%
3158
 
0.2%
6151
 
0.2%
247
 
0.1%
ValueCountFrequency (%)
-1218
 
0.2%
087309
95.7%
1213
 
0.2%
247
 
0.1%
3158
 
0.2%
41106
 
1.2%
5228
 
0.3%
6151
 
0.2%
7317
 
0.3%
91452
 
1.6%
ValueCountFrequency (%)
91452
 
1.6%
7317
 
0.3%
6151
 
0.2%
5228
 
0.3%
41106
 
1.2%
3158
 
0.2%
247
 
0.1%
1213
 
0.2%
087309
95.7%
-1218
 
0.2%

carriageway_hazards
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1805940855
Minimum-1
Maximum9
Zeros87881
Zeros (%)96.4%
Negative208
Negative (%)0.2%
Memory size712.6 KiB
2022-02-22T15:07:49.141989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.149791454
Coefficient of variation (CV)6.366717113
Kurtosis47.72392671
Mean0.1805940855
Median Absolute Deviation (MAD)0
Skewness6.898491154
Sum16470
Variance1.322020389
MonotonicityNot monotonic
2022-02-22T15:07:49.366605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
087881
96.4%
91191
 
1.3%
21079
 
1.2%
7314
 
0.3%
1220
 
0.2%
-1208
 
0.2%
6155
 
0.2%
3151
 
0.2%
ValueCountFrequency (%)
-1208
 
0.2%
087881
96.4%
1220
 
0.2%
21079
 
1.2%
3151
 
0.2%
6155
 
0.2%
7314
 
0.3%
91191
 
1.3%
ValueCountFrequency (%)
91191
 
1.3%
7314
 
0.3%
6155
 
0.2%
3151
 
0.2%
21079
 
1.2%
1220
 
0.2%
087881
96.4%
-1208
 
0.2%

urban_or_rural_area
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size712.6 KiB
1
61737 
2
29448 
3
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
161737
67.7%
229448
32.3%
314
 
< 0.1%

Length

2022-02-22T15:07:49.468358image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-22T15:07:49.544387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
161737
67.7%
229448
32.3%
314
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

did_police_officer_attend_scene_of_accident
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size712.6 KiB
1
62442 
2
19638 
3
9118 
-1
 
1

Length

Max length2
Median length1
Mean length1.000010965
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row3
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
162442
68.5%
219638
 
21.5%
39118
 
10.0%
-11
 
< 0.1%

Length

2022-02-22T15:07:49.633152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-22T15:07:49.712487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
162443
68.5%
219638
 
21.5%
39118
 
10.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

trunk_road_flag
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size712.6 KiB
2
79222 
-1
 
6710
1
 
5267

Length

Max length2
Median length1
Mean length1.073575368
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
279222
86.9%
-16710
 
7.4%
15267
 
5.8%

Length

2022-02-22T15:07:49.808483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-22T15:07:49.889299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
279222
86.9%
111977
 
13.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

lsoa_of_accident_location
Categorical

HIGH CARDINALITY

Distinct25931
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Memory size712.6 KiB
-1
 
3851
E01032739
 
70
E01004736
 
65
E01016012
 
50
E01033708
 
40
Other values (25926)
87123 

Length

Max length9
Median length9
Mean length8.704415619
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8160 ?
Unique (%)8.9%

Sample

1st rowE01004576
2nd rowE01003034
3rd rowE01004726
4th rowE01003617
5th rowE01004763

Common Values

ValueCountFrequency (%)
-13851
 
4.2%
E0103273970
 
0.1%
E0100473665
 
0.1%
E0101601250
 
0.1%
E0103370840
 
< 0.1%
E0100372839
 
< 0.1%
E0101864838
 
< 0.1%
E0102180037
 
< 0.1%
E0100472737
 
< 0.1%
E0100244436
 
< 0.1%
Other values (25921)86936
95.3%

Length

2022-02-22T15:07:49.975014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
13851
 
4.2%
e0103273970
 
0.1%
e0100473665
 
0.1%
e0101601250
 
0.1%
e0103370840
 
< 0.1%
e0100372839
 
< 0.1%
e0101864838
 
< 0.1%
e0102180037
 
< 0.1%
e0100472737
 
< 0.1%
e0103326936
 
< 0.1%
Other values (25921)86936
95.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-02-22T15:07:35.475973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:09.321093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:13.170492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:16.982491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:20.663288image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:24.336968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:27.952784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:32.014139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:36.166798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:40.220073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:44.064508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:47.953151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:51.848739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:55.930659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:00.029322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:04.048654image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:07.829331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:11.772469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:15.703269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:19.764612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:23.788024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:27.697703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:31.460712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:35.633968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:09.536396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:13.461758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:17.134733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:20.825989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:24.483295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:28.106848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:32.184789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:36.321359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:40.373391image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:44.234641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:48.114777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:52.025836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:56.102127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:00.207623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:04.211355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:07.993917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:11.925488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:15.882636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:19.943345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:23.968286image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:27.849216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:31.756551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:35.817176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:09.731641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:13.627107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:17.431153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:20.975887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:24.645349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:28.262602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:32.368751image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:36.497659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:40.548595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:44.406648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:48.284732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:52.186117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:56.280685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:00.391950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:04.386275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:08.168529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:12.091924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:16.062200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:20.128516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:24.133852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:28.015769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:31.931253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:36.103396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:09.925596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:13.786369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:17.573294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:21.125205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:24.791247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:28.415328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:32.538664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:36.666013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:40.700548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:44.568257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:48.453237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:52.356724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:56.435262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:00.554420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:04.542465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:08.328528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:12.254508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:16.219789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:20.293178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:24.299917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:28.169798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:32.081158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:36.257092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:10.125559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:13.953105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:17.722950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:21.409900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:24.937258image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:28.631251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:32.717976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:36.820568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:40.856839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:44.730465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:48.612558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:52.519804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:56.613349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:00.721054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:04.698458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:08.489131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:12.396477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:16.386468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:20.472467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:24.458473image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:28.317537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:32.243695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:36.415019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:10.279485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:14.108327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:17.877023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:21.570484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:25.221434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:28.869200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:32.896512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:36.991873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:41.019112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:44.899801image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:48.783185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:52.683885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:56.778784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:00.887732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:04.861139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:08.697469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:12.558470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:16.553491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:20.643632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:24.625108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:28.469232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:32.414477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:36.589296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:10.438061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:14.265485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:18.030959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:21.722327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:25.369813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:29.201844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:33.064654image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:37.148139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:41.173078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:45.063586image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:48.954063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:52.861033image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:06:56.942785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:01.054330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:07:05.027228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-02-22T15:07:28.636301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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Correlations

2022-02-22T15:07:50.137655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-22T15:07:50.575905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-22T15:07:51.016806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-22T15:07:51.413611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-02-22T15:07:51.647748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-22T15:07:39.561973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-22T15:07:41.169460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-02-22T15:07:41.931630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-02-22T15:07:42.281785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

accident_indexaccident_yearaccident_referencelocation_easting_osgrlocation_northing_osgrlongitudelatitudepolice_forceaccident_severitynumber_of_vehiclesnumber_of_casualtiesdateday_of_weektimelocal_authority_districtlocal_authority_ons_districtlocal_authority_highwayfirst_road_classfirst_road_numberroad_typespeed_limitjunction_detailjunction_controlsecond_road_classsecond_road_numberpedestrian_crossing_human_controlpedestrian_crossing_physical_facilitieslight_conditionsweather_conditionsroad_surface_conditionsspecial_conditions_at_sitecarriageway_hazardsurban_or_rural_areadid_police_officer_attend_scene_of_accidenttrunk_road_flaglsoa_of_accident_location
02020010219808202010219808521389.0175144.0-0.25400151.462262131104/02/2020309:0010E09000032E09000032606200-1609919900132E01004576
12020010220496202010220496529337.0176237.0-0.13925351.470327131227/04/2020213:559E09000022E090000223303662092600411100112E01003034
22020010228005202010228005526432.0182761.0-0.17871951.529614131101/01/2020401:251E09000033E090000335063031600041200112E01004726
32020010228006202010228006538676.0184371.0-0.00168351.541210121101/01/2020401:5017E09000025E090000253116300-1600441100112E01003617
42020010228011202010228011529324.0181286.0-0.13759251.515704131201/01/2020402:251E09000033E0900003334063034500041100112E01004763
52020010228012202010228012537193.0177105.0-0.02588051.476278131101/01/2020401:307E09000023E090000233220922034600041100112E01003304
62020010228014202010228014539764.0179234.00.01195951.494780132101/01/2020403:436E09000011E090000116063034600041200112E01001667
72020010228017202010228017536115.0182297.0-0.03939051.523195122101/01/2020403:005E09000030E0900003031133094600048200112E01004261
82020010228018202010228018530876.0191335.0-0.11150051.605653132101/01/2020404:0031E09000014E09000014310562094600041100112E01002102
92020010228020202010228020529718.0192342.0-0.12784051.614971132101/01/2020403:2532E09000010E0900001034063306234060541100112E01001530

Last rows

accident_indexaccident_yearaccident_referencelocation_easting_osgrlocation_northing_osgrlongitudelatitudepolice_forceaccident_severitynumber_of_vehiclesnumber_of_casualtiesdateday_of_weektimelocal_authority_districtlocal_authority_ons_districtlocal_authority_highwayfirst_road_classfirst_road_numberroad_typespeed_limitjunction_detailjunction_controlsecond_road_classsecond_road_numberpedestrian_crossing_human_controlpedestrian_crossing_physical_facilitieslight_conditionsweather_conditionsroad_surface_conditionsspecial_conditions_at_sitecarriageway_hazardsurban_or_rural_areadid_police_officer_attend_scene_of_accidenttrunk_road_flaglsoa_of_accident_location
9118920209910236212020991023621341791.0733677.0-2.94694756.4915589921110/11/2020310:30918S12000042S12000042607203460001920012-1-1
9119020209910238802020991023880342979.0731116.0-2.92709556.4686999931114/12/2020217:00918S12000042S12000042606309460004120012-1-1
9119120209910240392020991024039298547.0696827.0-3.63477556.1532909932115/09/2020307:10934S12000024S1200002439776300-160241110021-1-1
9119220209910242092020991024209294074.0581458.0-3.66227455.1161619932201/08/2020713:27917S12000006S120000063766609460001110021-1-1
9119320209910245262020991024526286242.0717023.0-3.84143956.3318649931117/11/2020312:00934S12000024S12000024609600-160001220722-1-1
9119420209910270642020991027064343034.0731654.0-2.92632056.4735399922112/08/2020414:30918S12000042S120000424959630144959001110011-1-1
9119520209910295732020991029573257963.0658891.0-4.26756555.8023539931113/11/2020615:05922S12000011S12000011609303460001110012-1-1
9119620209910302972020991030297383664.0810646.0-2.27190357.1863179922115/04/2020412:42910S12000033S1200003349796608-160001110021-1-1
9119720209910309002020991030900277161.0674852.0-3.96875355.9509409932115/12/2020314:00932S12000044S12000044606303460001110012-1-1
9119820209910325752020991032575240402.0681950.0-4.56104056.0038439931125/08/2020313:50916S12000039S12000039606300-160001110211-1-1

Duplicate rows

Most frequently occurring

accident_yearlocation_easting_osgrlocation_northing_osgrlongitudelatitudepolice_forceaccident_severitynumber_of_vehiclesnumber_of_casualtiesdateday_of_weektimelocal_authority_districtlocal_authority_ons_districtlocal_authority_highwayfirst_road_classfirst_road_numberroad_typespeed_limitjunction_detailjunction_controlsecond_road_classsecond_road_numberpedestrian_crossing_human_controlpedestrian_crossing_physical_facilitieslight_conditionsweather_conditionsroad_surface_conditionsspecial_conditions_at_sitecarriageway_hazardsurban_or_rural_areadid_police_officer_attend_scene_of_accidenttrunk_road_flaglsoa_of_accident_location# duplicates
02020443811.0434219.0-1.33628153.802451331124/02/2020207:45204E08000035E08000035213700-1600015500211E010112972